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Muhammad Taufiq Nuruzzaman
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JISKa (Jurnal Informatika Sunan Kalijaga)
Published by Universitas Islam Negeri Sunan Kalijaga Yogyakarta
ISSN : 25275836     EISSN : 25280074     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Library & Information Science
JISKa (Jurnal Informatika Sunan Kalijaga) adalah jurnal yang mencoba untuk mempelajari dan mengembangkan konsep Integrasi dan Interkoneksi Agama dan Informatika yang diterbitkan oleh Departemen Teknik Informasi UIN Sunan Kalijaga Yogyakarta. JISKa menyediakan forum bagi para dosen, peneliti, mahasiswa dan praktisi untuk menerbitkan artikel penelitiannya, mengkaji artikel dari para kontributor, dan teknologi baru yang berkaitan dengan informatika dari berbagai disiplin ilmu
Arjuna Subject : -
Articles 7 Documents
 1 
Search results for , issue "Vol. 9 No. 2 (2024): Mei 2024" : 7 Documents clear
Analisa Jejaring Sosial Terhadap Fenomena Cyberbullying Fandom K-Pop pada Sosial Media Twitter Ghufron, Mohammad Iqbal; Supriyati, Endang; Listyorini, Tri
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 9 No. 2 (2024): Mei 2024
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2024.9.2.79-93

Abstract

This study examines cyberbullying among K-pop fandoms through social network analysis (SNA) using data from Twitter, a social media platform. The phenomenon of K-pop gaining global popularity also brings negative impacts, such as cyberbullying, which can affect the psychological well-being of victims. Using R Studio and Gephi analysis tools, this study applied centrality values, including degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality, to identify influential accounts in the spread of the cyberbullying phenomenon. This analysis provides insight into the interaction and influence between Twitter user accounts in the context of cyberbullying. The main objective of this research is to paint a picture of the cyberbullying phenomenon involving various K-pop fandoms and identify the accounts that play an essential role in the related communication network.
Analisis Keamanan Data Pelanggan dalam Menghadapi Tantangan Penggunaan Marketplace Dewantara, Rizki; Bintang, Rauhulloh Ayatulloh Khomeini Noor; Gatra, Rahmadhan
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 9 No. 2 (2024): Mei 2024
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2024.9.2.94-104

Abstract

The advent of the digital economy makes commerce more accessible to everyone. Example: an online marketplace app that simplifies buying and selling. The marketplace app's useful features and ease of use attract many users. The marketplace app's functionality and convenience have been enhanced to meet consumer expectations and prioritize consumer data protection. This study investigates how customers protect their data when shopping online and utilizing marketplace apps. Environmental and social influences, personal data security facilities, the goal of utilizing the marketplace, and awareness of customer data security when using the marketplace application were asked of 70 random sample participants. The questionnaires had 16 Guttman scale questions. According to the report, 81.42% of customers trust the marketplace app to protect their data. Likewise, 88.57% of customers strongly believe that the marketplace application they use secures their personal information, indicating that this is related to their marketplace service needs.
Deep Learning dalam Prediksi Kebiasaan Merokok di Inggris Guna Mendukung Kebijakan Kesehatan Masyarakat yang Lebih Efektif Prabaswara, Muhammad Arden; Pratama, Kalistus Haris; Majid, Desva Fitranda; Liantoni, Febri
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 9 No. 2 (2024): Mei 2024
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2024.9.2.105-111

Abstract

Smoking is a common practice throughout the world, where a person smokes and inhales the smoke produced from burning tobacco or other tobacco products. This action has become a significant global health issue because of the various health risks. This activity is often considered an addictive habit because nicotine, the psychoactive compound in tobacco, can cause physical and psychological dependence. This research applies Deep Learning methods to predict data on smoking habits in the UK. The dataset used in this research includes information about gender, age, marital status, highest level of education, nationality, ethnicity, income, and region. Through this research using Deep Learning methods, we can examine a complex data set that describes Smoking Habits in the UK. Based on trials with a dataset of 1,691 items, an accuracy of 78% was obtained. This research can provide important insights into the effectiveness of anti-smoking policies that have been implemented and help plan further actions to reduce the prevalence of smoking and its negative impact on society.
Analisis dan Optimalisasi Performa Algoritma Gaussian Naive Bayes pada Prediksi Metabolic Syndrome Menggunakan SMOTE Fauziyah, Nadiyah Jihan; Rahmania, Fadilla; Daniyal, Muhammad; Sari, Nur Fitriyah Ayu Tunjung
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 9 No. 2 (2024): Mei 2024
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2024.9.2.112-122

Abstract

Metabolic syndrome is a complex global health problem, with symptoms such as abdominal obesity, insulin resistance, high blood pressure, high blood sugar, and abnormal blood lipids. With this global challenge, several studies have attempted to predict these diseases using machine learning methods. However, often, predictions about a disease result in data imbalance where minority classes are underrepresented. To balance the class proportions, the Synthetic Minority Over-sampling Technique (SMOTE) method replicates the minority class samples. In this research, the technique applied to predict is the Gaussian Naive Bayes (GNB) algorithm. The results show an increase in prediction accuracy by 0.2 from 0.81 to 0.83. This study confirms the critical role of the SMOTE oversampling method in machine learning using the Gaussian Naive Bayes (GNB) algorithm in Metabolic Syndrome prediction and its positive impact on diagnostic efficiency and public health.
Ensemble Learning pada Kategorisasi Produk E-Commerce Menggunakan Teknik Boosting Sepbriant, Genta Dwigi; Utomo, Danang Wahyu
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 9 No. 2 (2024): Mei 2024
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2024.9.2.123-133

Abstract

The development of e-commerce significantly contributes to technological advancement, especially for businesses adopting the concept. The growth of e-commerce has seen a significant increase, reaching 196.47 million users in 2023. In e-commerce, a wide range of product variations is provided to users, which can lead to errors or confusion in product selection. Product categorization is crucial in e-commerce to assist users in navigating efficiently. However, manual categorization is less effective as it can be time-consuming. This study aims to clarify the factors of concern in grouping using the K-Nearest Neighbors (KNN) algorithm in product categorization on the e-commerce platform. This research focuses on whether the novelty lies in the implemented algorithm, the variables used, or the applied grouping parameters. This work applies the XGBoost algorithm to improve the effectiveness of product categorization in e-commerce through ensemble learning approaches. The research findings indicate that boosting algorithms like XGBoost outperform individual algorithms like KNN regarding classification accuracy. This proves that ensemble learning approaches may greatly enhance product classification in e-commerce. The testing process of the implemented e-commerce system in this study also provides confidence in the theoretical and practical benefits of applying this research to enhance efficiency and user experience in product categorization on the e-commerce platform.
Klasterisasi Jumlah Penduduk Provinsi Jawa Timur Tahun 2021-2023 Menggunakan Algoritma K-Means Aryanto, Risqi Pradana; Nilogiri, Agung; Wardoyo, Ari Eko
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 9 No. 2 (2024): Mei 2024
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2024.9.2.134-146

Abstract

Understanding the population data of a region is crucial for policy development and strategic planning. East Java Province, the second-largest province in Indonesia, has undergone significant population growth from 2021 to 2023. Uneven growth poses challenges in resource and infrastructure management. The K-Means algorithm clusters population data into several groups based on specific characteristics. The Elbow method is used to determine the optimal number of clusters, ensuring the accuracy of the analysis. This research aims to analyze and cluster the population distribution in each city in East Java Province, providing a more detailed and accurate depiction. The research findings reveal three significant clusters. Cluster 0 includes 21 towns, Cluster 1 comprises 4, and Cluster 2 encompasses 13. These findings have important implications for targeted development policy formulation at the city level in East Java Province. Additionally, this study contributes to the development of demographic analysis and population management, using valid methods and consistent results between RapidMiner and manual calculations. In conclusion, this research provides a solid foundation for more effective development policy formulation in East Java Province, offering essential information for sustainable population management.
Deteksi Pelanggaran pada Zebra Cross dengan Water Spray dan Buzzer berbasis IoT Firdaus, Dina Uzlifatul; Christanto, Febrian Wahyu
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 9 No. 2 (2024): Mei 2024
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2024.9.2.147-158

Abstract

A zebra crossing is a road marking indicating a crossing path for pedestrians. Zebra crossings are directly used to signal drivers to stop at the line boundaries. Because the zebra crossing functions as a crossing area, pedestrians and motorized vehicle drivers must understand and obey existing traffic signs. According to data from the WHO (World Health Organization), 270,000 pedestrians die every year or around 22% of all victims who die due to road accidents. An ESP32-Cam microcontroller, an E18-D80NK Infrared Proximity Sensor, water spray and buzzer approaches, and the prototype development method were used to design a system for detecting crossing violations at zebra crossings to address this issue. The Infrared Proximity sensor will automatically detect when a crossing violation occurs, then the water spray will spray water, and the buzzer will make a sound as a warning sign to obey traffic. ESP32-Cam functions as an image capturer if a crossing violation has occurred and is automatically sent to the Telegram Bot. The confusion matrix test tested the research results with an accuracy value of 83.33%, a precision value of 83.33%, and a recall value of 88.23%.

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